Book Image

Data Analysis with Python

By : David Taieb
Book Image

Data Analysis with Python

By: David Taieb

Overview of this book

Data Analysis with Python offers a modern approach to data analysis so that you can work with the latest and most powerful Python tools, AI techniques, and open source libraries. Industry expert David Taieb shows you how to bridge data science with the power of programming and algorithms in Python. You'll be working with complex algorithms, and cutting-edge AI in your data analysis. Learn how to analyze data with hands-on examples using Python-based tools and Jupyter Notebook. You'll find the right balance of theory and practice, with extensive code files that you can integrate right into your own data projects. Explore the power of this approach to data analysis by then working with it across key industry case studies. Four fascinating and full projects connect you to the most critical data analysis challenges you’re likely to meet in today. The first of these is an image recognition application with TensorFlow – embracing the importance today of AI in your data analysis. The second industry project analyses social media trends, exploring big data issues and AI approaches to natural language processing. The third case study is a financial portfolio analysis application that engages you with time series analysis - pivotal to many data science applications today. The fourth industry use case dives you into graph algorithms and the power of programming in modern data science. You'll wrap up with a thoughtful look at the future of data science and how it will harness the power of algorithms and artificial intelligence.
Table of Contents (16 chapters)
Data Analysis with Python
Contributors
Preface
Other Books You May Enjoy
3
Accelerate your Data Analysis with Python Libraries
Index

Part 4 – Adding scalability with Apache Kafka and IBM Streams Designer


Note

Note: This section is optional. It demonstrates how to re-implement parts of the data pipeline with cloud-based streaming services to achieve greater scalability

Implementing the entire data pipeline in a single Notebook gave us high productivity during development and testing. We can experiment with the code and test the changes very rapidly with a very small footprint. Also, performances have been reasonable because we have been working with a relatively small amount of data. However, it is quite obvious that we wouldn't use this architecture in production and the next question we need to ask ourselves is where are the bottlenecks that would prevent the application from scaling as the quantity of streaming data coming from Twitter increases dramatically.

In this section, we identify two areas for improvement:

  • In the Tweepy stream, the incoming data is sent to the RawTweetsListener instance for processing using the...